Deploying an executable with historical performance data

ABSTRACT

Techniques for incorporating performance data into an executable file for an application are described. Embodiments monitor performance of an application while the application is running. Additionally, historical execution characteristics of the application are determined based upon the monitored performance and one or more system characteristics of a node on which the application was executed on. Embodiments also incorporate the historical execution characteristics into executable file for the application, such that the historical execution characteristics can be used to manage subsequent executions of the application.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 13/363,647, filed Feb. 1, 2012. The aforementioned relatedpatent application is herein incorporated by reference in its entirety.

BACKGROUND

Embodiments of the present invention generally relate to managingapplications. Specifically, the invention relates to packaging anexecutable with performance information for use in managing execution ofthe executable.

While computer databases have become extremely sophisticated, thecomputing demands placed on database systems have also increased at arapid pace. Database systems are typically configured to separate theprocess of storing data from accessing, manipulating or using datastored in the database. More specifically, databases use a model wheredata is first stored, then indexed, and finally queried. However, thismodel cannot meet the performance requirements of some real-timeapplications. For example, the rate at which a database system canreceive and store incoming data limits how much data can be processed orotherwise evaluated. This, in turn, can limit the ability of databaseapplications to process large amounts of data in real-time.

SUMMARY

Embodiments provide a method, system, and computer program product forincorporating performance data into an executable file for anapplication. The method, system, and computer program product includemonitoring performance of an application while the application isrunning Additionally, the method, system, and computer program productinclude determining historical execution characteristics of theapplication based on the monitored performance and one or more systemcharacteristics of a node on which the application was executed on. Themethod, system, and computer program product also include incorporatingthe historical execution characteristics into executable file for theapplication, such that the historical execution characteristics can beused to manage subsequent executions of the application.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

So that the manner in which the above recited aspects are attained andcan be understood in detail, a more particular description ofembodiments of the invention, briefly summarized above, may be had byreference to the appended drawings.

It is to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIGS. 1A-1B illustrate a computing infrastructure configured to executea stream computing application, according to one embodiment describedherein.

FIG. 2 is a more detailed view of the compute node of FIGS. 1A-1B,according to one embodiment described herein.

FIG. 3 is a more detailed view of the server computing system of FIG. 1,according to one embodiment described herein.

FIGS. 4A-B are diagrams illustrating systems configured with a PEmanagement component, according to embodiments described herein.

FIG. 5 is a flow diagram illustrating a method for incorporatingperformance information into an executable, according to one embodimentdescribed herein.

FIG. 6 is a flow diagram illustrating a method for deploying aprocessing element, according to one embodiment described herein.

DETAILED DESCRIPTION

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in milliseconds. Constructing an applicationusing this type of processing has opened up a new programming paradigmthat will allow for a broad variety of innovative applications, systemsand processes to be developed, as well as present new challenges forapplication programmers and database developers.

In a stream computing application, operators are connected to oneanother such that data flows from one operator to the next (e.g., over aTCP/IP socket). Scalability is reached by distributing an applicationacross nodes by creating many small executable pieces of code (i.e.,processing elements), each of one which contains one or more processingmodules (i.e., operators). These processing elements can also bereplicated on multiple nodes with load balancing among them. Operatorsin a stream computing application can be fused together to form aprocessing element. Additionally, multiple processing elements can begrouped together to form a job. Doing so allows processing elements toshare a common process space, resulting in much faster communicationbetween operators than is available using inter-process communicationtechniques (e.g., using a TCP/IP socket). Further, processing elementscan be inserted or removed dynamically from an operator graphrepresenting the flow of data through the stream computing application.

One advantage of stream computing applications is that they allow theuser to granularly control the process flow of data through theapplication. In other words, the user may designate specific operatorsto perform various operations on the incoming data, and may dynamicallyalter the stream computing application by modifying the operators andthe order in which they are performed. Additionally, stream computingapplications are able to handle large volumes of data.

However, because stream computing applications often deal with largevolumes of data, the processing of which is spread over multipleprocessing elements across multiple compute nodes, an operator may needto produce an output faster than it is able. Instead of requiring anoperator to generate output data by processing currently received inputdata, an operator may instead output predetermined data. Thispredetermined data may be based on, for example, an average of theoutput data that was previously processed and transmitted by theoperator. Moreover, the operator may only transmit predicted output dataif the previously processed output data falls within an acceptablerange. That is, if the previous output data is deterministic. Anoperator, or data flowing out of the operator, is “deterministic” if thevalues of the output data can be predicted with some minimum amount ofconfidence. For example, output data may be predictable or deterministicbecause a certain input always yields a certain output or because theoutput data typically has a value within a certain range—e.g., theoutput values for an operator are within a predefined range 80% of thetime. Once the output data is deemed deterministic, using the predictedoutput data may allow the operator to transmit output data faster, orwith less processing, than it otherwise would be able.

Moreover, the operator may output predetermined data only if there is aneed to limit or stop processing received input data. For example, thestream computing application may be experiencing backpressure.“Backpressure” is a term used to describe one or more operators that areunable to transmit or receive additional data because either theirbuffer or a buffer associated with a downstream operator is full. In thecase of some real-time applications, the operator may trade accuracy forincreased data throughput where the time required for data to propagatethrough the stream computing application is an important factor.

One advantage of stream computing application is that processingelements can be quickly moved into and out of the operator graph. Assuch, it may optimal in particular stream computing applications forcertain processing elements to be offline until the processing elementsare needed. However, because the operators within these processingelements have a requisite amount of data that must be received from oneor more upstream operators before the operators can begin generatingoutput data, there may be a delay once the processing elements arestarted before the operators within the processing elements can generatemeaningful output or even output values at all.

One consideration in stream computing, or more generally in anydistributed computing application, is the deployment of a particularexecutable on a suitable node. That is, certain applications (e.g., aprocessing element in a stream computing application) may performoptimally on certain configurations of hardware and software andparticular applications may not operate at all without certain minimumhardware and software requirements. As such, embodiments providetechniques for managing execution of a processing element are described.Embodiments may receive a first processing element for deployment. Here,the first processing element is an executable application thatencapsulates performance information describing executioncharacteristics of the executable application. Additionally, systeminformation for each of a plurality of nodes may be retrieved.Embodiments may then select one or the plurality of nodes to deploy thereceived first processing element to based on the retrieved systeminformation and the execution characteristics. The first processingelement may then be deployed for execution on the selected node. Doingso enables the processing element to be deployed to a suitable node forexecution without requiring any additional information beyond theexecutable application.

Additionally, embodiments may use the historical performance informationwithin the executable application (or portions of a stream computingapplication—e.g., within a processing element) for otherdeployment-related functions. For instance, embodiments may use theperformance information together with the system information for thenode on which the application is deployed in order to predict problemswith the application before the problems arise. For example, ifembodiments detect the workload on the node has substantially increasedsuch that the node no longer meets specifications defined by thehistorical performance information, embodiments could determine that theapplication is likely to experience problems running on the current nodeunless some remedial action is taken. As another example, embodimentscould use the historical performance information to determine that theapplication is not currently performing as efficiently as it has onprevious executions. Embodiments could perform one or more remedialactions, such as generating a notification to an administrator of thesystem or altering the deployment of the application. For instance,embodiments could migrate a portion of the application (e.g., aprocessing element) to a different, more suitable node within thedistributed computing system. As another example, if the application isa processing element within a stream computing application, embodimentscould clone a second instance of the processing element onto another,different node within the distributed computing system and could modifyan operator graph for the stream computing application to load balancebetween the two processing elements. Advantageously, doing so allowspotential problems within a distributed environment to be detected andcorrected, oftentimes before an actual problem arises.

FIGS. 1A-1B illustrate a computing infrastructure configured to executea stream computing application, according to one embodiment of theinvention. As shown, the computing infrastructure 100 includes amanagement system 105 and a plurality of compute nodes 13 ₀₁₋₄, eachconnected to a communications network 120. Also, the management system105 includes an operator graph 132 and a stream manager 134. Asdescribed in greater detail below, the operator graph 132 represents astream computing application beginning from one or more sourceprocessing elements (PEs) through to one or more sink PEs. This flowfrom source to sink is also generally referred to herein as an executionpath. However, an operator graph may be a plurality of linked togetherexecutable units (i.e., processing elements) with or without a specifiedsource or sink. Thus, an execution path would be the particular linkedtogether execution units that data traverses as it propagates throughthe operator graph.

Generally, data attributes flow into a source PE of a stream computingapplication and are processed by that PE. Typically, processing elementsreceive an N-tuple of data attributes from the stream as well as emit anN-tuple of data attributes into the stream (except for a sink PE wherethe stream terminates). Of course, the N-tuple received by a processingelement need not be the same N-tuple sent downstream. Additionally, theprocessing elements could be configured to receive or emit data informats other than a tuple (e.g., the processing elements could exchangedata marked up as XML documents). Furthermore, each processing elementmay be configured to carry out any form of data processing functions onthe received tuple, including, for example, writing to database tablesor performing other database operations such as data joins, splits,reads, etc., as well as performing other data analytic functions oroperations.

The stream manager 134 may be configured to monitor a stream computingapplication running on the compute nodes 130 ₁₋₄, as well as to changethe structure of the operator graph 132. The stream manager 134 may moveprocessing elements (PEs) from one compute node 130 to another, forexample, to manage the processing loads of the compute nodes 130 in thecomputing infrastructure 100. Further, stream manager 134 may controlthe stream computing application by inserting, removing, fusing,un-fusing, or otherwise modifying the processing elements (or whatdata-tuples flow to the processing elements) running on the computenodes 130 ₁₋₄. One example of a stream computing application is IBM®'sInfoSphere® Streams (note that InfoSphere® is a trademark ofInternational Business Machines Corporation, registered in manyjurisdictions worldwide).

FIG. 1B illustrates an example operator graph that includes tenprocessing elements (labeled as PE1-PE10) running on the compute nodes130 ₁₋₄. Of note, because a processing element is a collection of fusedoperators, it is equally correct to describe the operator graph asexecution paths between specific operators, which may include executionpaths to different operators within the same processing element. FIG. 1Billustrates execution paths between processing elements for the sake ofclarity. While a processing element may be executed as an independentlyrunning process with its own process ID (PID) and memory space, multipleprocessing elements may also be fused to run as single process or job(with a PID and memory space). In cases where two (or more) processingelements are running independently, inter-process communication mayoccur using a “transport” (e.g., a network socket, a TCP/IP socket, orshared memory). However, when processes are fused together, the fusedprocessing elements can use more rapid communication techniques forpassing tuples (or other data) among processing elements (and operatorsin each processing element).

As shown, the operator graph begins at a source 135 (that flows into theprocessing element labeled PE1) and ends at sink 140 ₁₋₂ (that flowsfrom the processing elements labeled as PE6 and PE10). Compute node 130₁ includes the processing elements PE1, PE2 and PE3. Source 135 flowsinto the processing element PE1, which in turn emits tuples that arereceived by PE2 and PE3. Of note, although the operators within theprocessing elements are not shown in FIG. 1B, in one embodiment the datatuples flow between operators within the processing elements rather thanbetween the processing elements themselves. For example, one or moreoperators within PE1 may split data attributes received in a tuple andpass some data attributes to one or more other operators within PE2,while passing other data attributes to one or more additional operatorswithin PE3. Data that flows to PE2 is processed by the operatorscontained in PE2, and the resulting tuples are then emitted to PE4 oncompute node 130 ₂. Likewise, the data tuples emitted by PE4 flow tosink PE6 140 ₁. Similarly, data tuples flowing from PE3 to PE5 (i.e.,from operator(s) within PE3 to operator(s) within PE5) also reach sinkPE6 140 ₁. Thus, in addition to being a sink for this example operatorgraph, PE6 could be configured to perform a join operation, combiningtuples received from PE4 and PE5. This example operator graph also showsdata tuples flowing from PE3 to PE7 on compute node 130 ₃, which itselfshows data tuples flowing to PE8 and looping back to PE7. Data tuplesemitted from PE8 flow to PE9 on compute node 130 ₄, which in turn emitstuples to be processed by sink PE10 140 ₂.

Furthermore, although embodiments of the present invention are describedwithin the context of a stream computing application, this is not theonly context relevant to the present disclosure. Instead, such adescription is without limitation and is for illustrative purposes only.Of course, one of ordinary skill in the art will recognize thatembodiments of the present invention may be configured to operate withany computer system or application capable of performing the functionsdescribed herein. For example, embodiments of the invention may beconfigured to operate in a clustered environment with a standarddatabase processing application.

FIG. 2 is a more detailed view of the compute node 130 of FIGS. 1A-1B,according to one embodiment of the invention. As shown, the compute node130 includes, without limitation, at least one CPU 205, a networkinterface 215, an interconnect 220, a memory 225, and storage 230. Thecompute node 130 may also include an I/O devices interface 210 used toconnect I/O devices 212 (e.g., keyboard, display and mouse devices) tothe compute node 130.

Each CPU 205 retrieves and executes programming instructions stored inthe memory 225. Similarly, the CPU 205 stores and retrieves applicationdata residing in the memory 225. The interconnect 220 is used totransmit programming instructions and application data between each CPU205, I/O devices interface 210, storage 230, network interface 215, andmemory 225. CPU 205 is included to be representative of a single CPU,multiple CPUs, a single CPU having multiple processing cores, and thelike. The memory 225 is generally included to be representative of arandom access memory (e.g., DRAM or Flash). Storage 230, such as a harddisk drive, solid state disk (SSD), or flash memory storage drive, maystore non-volatile data.

In this example, the memory 225 includes a plurality of processingelements 235. The processing elements 235 include a collection ofoperators 240 and historical performance information 245. The historicalperformance information 245 generally represents data collected fromprevious executions of the respective processing element 235 on one ofthe nodes 130 of the stream computing system. For example, historicalperformance information 245 could specify system characteristics ofcompute nodes on which the respective processing element 235 has beensuccessfully executed and could further specify system characteristicsof computer node on which the respective processing element 235 did notexecute successfully. Such characteristics may include, withoutlimitation, hardware attributes of the compute nodes (e.g., processortype, processor count, memory type, amount of memory, etc.), softwareattributes of the compute nodes (e.g., applications installed on thecompute nodes, versions of the applications, etc.) and performancecharacteristics of the compute nodes (e.g., system workload, networkload, etc.).

Additionally, as noted above, each operator 240 may provide a smallchunk of executable code configured to process data flowing into aprocessing element (e.g., PE 235) and to emit data to other operators240 in that PE and to other processing elements in the stream computingapplication. Such processing elements may be on the same compute node130 or on other compute nodes accessible over the data communicationsnetwork 120. Memory 225 may also contain stream connection data (notshown) which represents the connections between PEs on compute node 130(e.g., a TCP/IP socket connection between two separate PEs 235), as wellas connections to other compute nodes 130 with upstream and ordownstream PEs in the stream computing application, also via TCP/IPsockets (or other inter-process data communication mechanisms).

As shown, storage 230 contains buffered stream data 260 and systeminformation 265. The buffered stream data 260 represents a storage spacefor data flowing into the compute node 105 from upstream processingelements (or from a data source for the stream computing application).For example, buffered stream data 260 may include data tuples waiting tobe processed by one of the PEs 235—i.e., a buffer. Buffered stream data260 may also store the results of data processing performed byprocessing elements 235 that will be sent to downstream processingelements. For example, a PE 235 may have to store tuples intended for adownstream PE 235 if that PE 235 already has a full buffer, which mayoccur when the operator graph is experiencing backpressure. The systeminformation 265 generally specifies characteristics of the compute node130. Such characteristics may include, without limitation, hardwareattributes of the compute node (e.g., processor type, processor count,memory type, amount of memory, etc.), software attributes of the computenode (e.g., applications installed on the compute nodes, versions of theapplications, etc.) and performance characteristics of the compute node(e.g., system workload, network load, etc.).

FIG. 3 is a more detailed view of the server computing system 105 ofFIG. 1, according to one embodiment of the invention. As shown, servercomputing system 105 includes, without limitation, a CPU 305, a networkinterface 315, an interconnect 320, a memory 325, and storage 330. Theclient system 130 may also include an I/O device interface 310connecting I/O devices 312 (e.g., keyboard, display and mouse devices)to the server computing system 105.

Like CPU 205 of FIG. 2, CPU 305 is configured to retrieve and executeprogramming instructions stored in the memory 325 and storage 330.Similarly, the CPU 305 is configured to store and retrieve applicationdata residing in the memory 325 and storage 330. The interconnect 320 isconfigured to move data, such as programming instructions andapplication data, between the CPU 305, I/O devices interface 310,storage unit 330, network interface 305, and memory 325. Like CPU 205,CPU 305 is included to be representative of a single CPU, multiple CPUs,a single CPU having multiple processing cores, and the like. Memory 325is generally included to be representative of a random access memory.The network interface 315 is configured to transmit data via thecommunications network 120. Although shown as a single unit, the storage330 may be a combination of fixed and/or removable storage devices, suchas fixed disc drives, removable memory cards, optical storage, SSD orflash memory devices, network attached storage (NAS), or connections tostorage area-network (SAN) devices.

As shown, the memory 325 stores a stream manager 134 configured with aPE management component 340. Additionally, the storage 330 includes aprimary operator graph 335. The stream manager 134 may use the primaryoperator graph 335 to route tuples to PEs 235 for processing. The streammanager 134 also includes a predictive startup component 340. Generally,the PE management component 340 is configured to select one of thecompute nodes 130 in the stream computing system on which to deploy aparticular processing element 235, based on historical performanceinformation 245 within the executable file for the processing element235. For example, the PE management component 340 could retrieve thehistorical performance information 245 from the executable for theprocessing element 235 and could further retrieve the system information265 from each of the compute nodes 130 within the stream computingenvironment. The PE management component 340 could then use thehistorical performance information 245 and the retrieved systeminformation 265 to select a suitable compute node 130 on which to deploythe particular processing element 235. Doing so allows for processingelements to be deployed on suitable compute nodes without requiring anyadditional information beyond the executable application for theprocessing element and system information describing characteristics ofthe compute nodes.

Additionally, the PE management component 340 could use the historicalperformance information 245 within the processing element 235 to predictproblems with the execution of the processing element 235, andoftentimes may do so before any problems actually arise. For example,the PE management component 340 could monitor system information 265 forthe compute node 130 on which the processing element 235 is deployed inorder to detect when the compute node 130 no longer matches the systemattributes of other compute nodes that have successfully executed theprocessing element previously, as specified in the historicalperformance data. Upon detecting that the attributes no longer match,the PE management component 340 could perform a remedial action for theprocessing element 235. For example, the PE management component 340could generate a notification to a system administrator describing thepotential problem. As another example, the PE management component 340could adjust the operator graph for the stream computing application inorder to reduce the load on the processing element 235. Advantageously,doing so allows for potential problems within the stream computingapplication to be detected and resolved before any actual problemsarise.

FIGS. 4A-B are diagrams illustrating systems configured with a PEmanagement component, according to embodiments described herein. Asshown in FIG. 4A, the system 400 includes a management system 410 andcompute nodes 420 ₁₋₂, connected via a network 430. The managementsystem 410 includes a processing element 235 for deployment onto one ofcompute nodes 420 ₁₋₂ and a PE management component 340. The processingelement executable 235 includes historical performance information 245.As discussed above, the historical performance information 245 generallyspecifies characteristics of compute nodes on which the processingelement 235 has previously been executed on. Additionally, theprocessing element 235 may include one or more operators (not shown),each of which may provide a small chunk of executable code configured toprocess data flowing into the processing element 235 and to emit data toother operators in that PE 235 and to other processing elements in thestream computing application.

Additionally, each of the compute nodes 425 ₁₋₂ includes systeminformation 425 ₁₋₂. As discussed above, the system information 425 ₁₋₂represents data describing characteristics of the respective computenode 420 ₁₋₂. Such characteristics may include, without limitation,hardware attributes of the compute node (e.g., processor type, processorcount, memory type, amount of memory, etc.), software attributes of thecompute node (e.g., applications installed on the compute nodes,versions of the applications, etc.) and performance characteristics ofthe compute node (e.g., system workload, network load, etc.).

In the depicted example, the PE management component 340 is configuredto select one of the compute nodes 420 ₁₋₂ to deploy the processingelement 235 to using the historical performance information 245 withinthe processing element executable 235. For example, the historicalperformance information 245 could specify that the processing element235 has successfully been deployed on nodes having 4 gigabytes ofavailable memory, but that the processing element 235 experiencedproblems when running on nodes having 1 gigabyte of available memory. Ifthe PE management component 340 then determines that the compute node420 ₁ has 8 gigabytes of available memory (i.e., based on the systeminformation 425 ₁) while the compute node 420 ₂ only has 512 megabytesof available memory (i.e., based on the system information 425 ₂), thePE management component 340 could determine that only the compute node420 ₁ is suitable for deploying the processing element 235 to.

Based on this determination, the PE management component 340 could thendeploy the processing element 235 to the compute node 420 ₁. An exampleof this is shown in the FIG. 4B. Similar to the FIG. 4A, the system 440includes a management system 410 and compute nodes 420 ₁₋₂, connectedvia a network 430. The management system 410 includes a PE managementcomponent 340. The processing element executable 235 includes historicalperformance information 245. As discussed above, the historicalperformance information 245 generally specifies characteristics ofcompute nodes on which the processing element 235 has previously beenexecuted on. Additionally, the processing element 235 may include one ormore operators (not shown), each of which may provide a small chunk ofexecutable code configured to process data flowing into the processingelement 235 and to emit data to other operators in that PE 235 and toother processing elements in the stream computing application.Additionally, each of the compute nodes 425 ₁₋₂ includes systeminformation 425 ₁₋₂. As discussed above, the system information 425 ₁₋₂represents data describing characteristics of the respective computenode 420 ₁₋₂.

In the depicted system 440, the PE management component 340 has selectedthe compute node 420 ₁ and accordingly has deployed the processingelement 235 (which includes the historical performance information 245)to the compute node 420 ₁. As discussed above, the PE managementcomponent 340 can also be configured to use the historical performanceinformation 245 within the processing element 235 together with thesystem information 425 ₁ to detect potential problems with executing theprocessing element 235 on the compute node 420 ₁. For example, the PEmanagement component 340 could detect that the workload of the computenode 420 ₁ has increased since the processing element 235 was deployedto the node 420 ₁. If the PE management component 340 then determinesthat the workload of the compute node 420 ₁ exceeds a threshold amountof workload for a system running the processing element 235 specified inthe historical performance information 245, the PE management component340 could perform a remedial action for the processing element 235.

For example, one such remedial action by the PE management component 340could be spawning a second instance of the processing element 235 on thecompute node 420 ₂. The PE management component 340 could then updatethe operator graph for the stream computing application to load balancebetween the processing element 235 and the second instance of theprocessing element. Doing so helps to alleviate the workload on theprocessing element 235 and thus may avoid any actual problems caused bythe increased workload on the node 420 ₁ from occurring. Additionally,embodiments may do so without requiring any additional informationbeyond the processing element 235 executable and information (e.g.,system information 425 ₁₋₂) describing characteristics of the computenodes 420 ₁₋₂.

As another example of a remedial action, upon determining a potentialproblem exists with the processing element 235 executing on the computenode 420 ₁ based on the historical performance information 245 and thesystem information 425 ₁, the PE management component 340 could select anew compute node 420 that is suitable to execute that the processingelement 235. For purposes of the present example, assume that the PEmanagement component 340 analyzes the historical performance information245 and the system information 425 ₂ and determines that the computenode 420 ₂ is suitable for hosting the processing element 235. The PEmanagement component 340 could then terminate the processing element 235executing on the compute node 420 ₁ and could spawn a new instance ofthe processing element 235 on the compute node 420 ₂. The PE managementcomponent 340 could then update the operator graph to account for thenewly created instance of the processing element 235 running on thecompute node 420 ₂. Advantageously, doing so migrates the processingelement 235 to a suitable node upon detecting a potential problem existswith the processing element's 235 current execution environment and mayeven do so before any actual problems with the processing element 235arise.

FIG. 5 is a flow diagram illustrating a method for incorporatingperformance information into an executable, according to one embodimentdescribed herein. As shown, the method 510 begins at step 510, where aprocessing element begins execution on a first compute node in a streamcomputing environment. The PE management component 340 then monitorsperformance characteristics of the processing element executing on thefirst compute node (step 515) and collect performance information forthe processing element (step 520). For example, the PE managementcomponent 340 could monitor a rate at which the processing elementreceives tuples of data from upstream processing elements and a rate atwhich the processing element processes incoming tuples of data. The PEmanagement component 340 could later use such information to, forinstance, determine whether the processing element is accumulating abacklog of data to process. That is, if the processing element isreceiving more tuples of data than the processing element can process ina given amount of time, the PE management component 340 could determinethat the processing element is underperforming on the first computenode.

Additionally, the PE management component 340 could monitorcharacteristics of the compute node on which the processing element isexecuting. For example, such characteristics could include a type ofprocessor on the compute node, a number of processors on the node, anamount of memory on the node, the type of the memory, an amount ofstorage on the node, the type of storage, network connectivitycharacteristics (e.g., network connections, network speed, networkcongestion, etc.), and system workload (e.g., processor usage, memoryusage, etc.). The PE management component 340 could then use suchinformation to determine whether other compute nodes are suitable forthe processing element to execute on. For example, assume that the PEmanagement component 340 determines the compute node on which theprocessing element is currently executing has 2 gigabytes of memory andthat the processing element is currently executing without any problems(e.g., no data backlog is accumulating, no errors or exceptions beinggenerated, etc.). Accordingly, the PE management component 340 coulddetermine that other systems having at least 2 gigabytes of memory couldbe suitable for executing the processing element. Furthermore, suchsystem data may be collected over an extended period of time duringwhich the processing element is executed across multiple compute nodes,so that the system specifications for suitable execution can be moreprecisely defined.

The PE management component 340 then incorporates the collectedperformance information into the executable file for the processingelement (step 525) and the method 500 ends. For example, the PEmanagement component 340 could store the collected performance using aplurality of global variables in the source code for the processingelement and could then compile the source code to create the executablefile for the processing element. Advantageously, by incorporating thehistorical performance information into the executable for theprocessing element, embodiments enable the processing element to selecta node on which it can be successfully deployed onto. Additionally, theprocessing element could even use the incorporated performanceinformation to detect potential problems that may arise during theexecution of the processing element.

FIG. 6 is a flow diagram illustrating a method for deploying aprocessing element, according to one embodiment described herein. Asshown, the method 600 begins at step 610, where the PE managementcomponent 340 receives a processing element for deployment on one ormore nodes in a stream computing environment. The PE managementcomponent 340 accesses historical performance data stored in theexecutable file for the processing element (step 615). For example, suchhistorical performance data could be collected and incorporated into theexecutable using the method 500 shown in FIG. 5 and discussed above inthe corresponding text. Additionally, as discussed above, the historicalperformance information generally specifies characteristics of othercompute nodes on which the received processing element has previouslybeen executed on and how the processing element performed when executedon the other compute nodes.

The PE management component 340 also retrieves system information foreach of a plurality of compute nodes (step 620). As discussed above, thesystem information represents data describing characteristics of therespective compute node. Such characteristics may include, withoutlimitation, hardware attributes of the compute node (e.g., processortype, processor count, memory type, amount of memory, etc.), softwareattributes of the compute node (e.g., applications installed on thecompute nodes, versions of the applications, etc.) and performancecharacteristics of the compute node (e.g., system workload, networkload, etc.).

In the depicted embodiment, the PE management component 340 then selectsa node on which to deploy the processing element based on the historicalperformance information and the system information (step 625). Forexample, the PE management component 340 could analyze the historicalperformance information to determine a set of system specificationsneeded to run the processing element (e.g., based on the systemspecifications of systems where the processing element has beensuccessfully executed and further based on the system specifications ofsystems where the processing element was not successfully executed) andcould compare this information against the system information for eachof the compute nodes in order to determine a suitable node to deploy theprocessing element to. The PE management component 340 then deploys theprocessing element to the selected node (step 630) and the method 600ends.

In the preceding, reference is made to embodiments of the invention.However, the invention is not limited to specific described embodiments.Instead, any combination of the following features and elements, whetherrelated to different embodiments or not, is contemplated to implementand practice the invention. Furthermore, although embodiments of theinvention may achieve advantages over other possible solutions and/orover the prior art, whether or not a particular advantage is achieved bya given embodiment is not limiting of the invention. Thus, the precedingaspects, features, embodiments and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s). Likewise, reference to“the invention” shall not be construed as a generalization of anyinventive subject matter disclosed herein and shall not be considered tobe an element or limitation of the appended claims except whereexplicitly recited in a claim(s).

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Embodiments of the invention may be provided to end users through acloud computing infrastructure. Cloud computing generally refers to theprovision of scalable computing resources as a service over a network.More formally, cloud computing may be defined as a computing capabilitythat provides an abstraction between the computing resource and itsunderlying technical architecture (e.g., servers, storage, networks),enabling convenient, on-demand network access to a shared pool ofconfigurable computing resources that can be rapidly provisioned andreleased with minimal management effort or service provider interaction.Thus, cloud computing allows a user to access virtual computingresources (e.g., storage, data, applications, and even completevirtualized computing systems) in “the cloud,” without regard for theunderlying physical systems (or locations of those systems) used toprovide the computing resources.

Typically, cloud computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g. an amount of storage space consumed by auser or a number of virtualized systems instantiated by the user). Auser can access any of the resources that reside in the cloud at anytime, and from anywhere across the Internet. In context of the presentinvention, a stream computing application configured with a PEmanagement component could execute across one or more nodes within thecloud. The PE management component within the stream computingapplication could retrieve a first processing element for deployment andcould access historical performance information within the firstprocessing element. The PE management component could further retrievesystem information for each of a plurality of nodes within the cloud andcould reference the system information together with the historicalperformance data in order to determine a suitable node onto which todeploy the processing element. Doing so helps to ensure optimal nodeplacement for processing elements within a stream computing applicationwhich users may access from any computing system attached to a networkconnected to the cloud (e.g., the Internet).

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. Each block of the block diagrams and/orflowchart illustrations, and combinations of blocks in the blockdiagrams and/or flowchart illustrations, can be implemented byspecial-purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

What is claimed is:
 1. A method for incorporating performance data intoan executable file for an application, comprising: monitoringperformance of the executable file for the application while theexecutable file is executing on a node; determining the performance dataof the application based on the monitored performance and one or moresystem characteristics of the node; incorporating the performance datainto the executable file for the application for subsequent retrievalfrom the executable file, further comprising: storing the performancedata in a plurality of global variables in a source code for theapplication; and compiling the source code including the performancedata stored in the plurality of global variables to create theexecutable file for the application, such that the performance data cansubsequently be retrieved from the executable file; deploying theexecutable file containing the incorporated performance data for theapplication to a first node for execution; retrieving the performancedata from the executable file for use in managing the execution of theexecutable file: and performing a remedial action for the deployedapplication, upon determining that a potential workload overflow problemexists for the deployed application based on s stem informationdescribing attributes of the first node and the retrieved performancedata.
 2. The method of claim 1, wherein the application is a processingelement in a stream computing application, and further comprising:retrieving system information for each of a plurality of nodes inclusiveof the node; selecting one or the plurality of nodes to deploy theprocessing element based on the retrieved system information and theperformance data incorporated into the executable file for theprocessing element; and deploying the processing element for executionon the selected node.
 3. The method of claim 2, wherein the processingelement is deployed alongside a plurality of other processing elements,and further comprising: establishing an operator graph of the pluralityof other processing elements and the deployed processing element, theoperator graph defining at least one execution path and wherein at leastone of the processing elements of the operator graph is configured toreceive data from at least one upstream processing element and transmitdata to at least one downstream processing element.
 4. The method ofclaim 1, wherein the performance data include at least one of: aprocessor type, a number of processors, an amount of memory, a type ofmemory, a type of storage, one or more network connectivitycharacteristics, and a measure of system workload.
 5. The method ofclaim 1, further comprising: determining a number of instances of theapplication that should be created, based on system information forsystems on which the instances of the application will be executed andfurther based on the performance data; and creating the determinednumber of instances of the application on the systems, such that loadcan be balanced between the created instances of the application.
 6. Themethod of claim 1, wherein performing a remedial action includes atleast one of: (i) spawning a second instance of the application on asecond node and discarding the deployed executable file on the firstnode, (ii) migrating the deployed executable file on the first node to asecond node, (iii) modifying an operator graph to reduce a workload ofthe deployed executable file, and (iv) modifying one or more operationsperformed by the deployed executable file.
 7. The method of claim 1,further comprising: generating a status notification for the deployedexecutable file, the status notification indicating a current amount ofworkload for the executable file and a maximum amount of workload forthe executable file, based on current workload information for the firstnode, the system information describing the attributes of the first nodeand the performance data incorporated into the executable file.